Quality by Design (QbD)

Quality by Design (QbD) is a strategic process for development and manufacturing.  It is meant to ensure that the intended performance of a final drug product is as expected – both in terms of purity and efficacy. To achieve this requires well-described objectives, and proper risk management.

Quality by Design  includes a thorough understanding of the technical process, the variables that affect the process and the performance envelope in which those variables remain in order to achieve the objectives. The advantages of QbD benefit both the consumer (since the product will be consistently safe and effective) and the manufacturer (since quality and costs will be well understood, controlled and predictable). 

The Quality by Design development process begins by carefully defining the requirements of the final product, including use targets, safety profiles and product efficacy goals. The steps in the process include:

• Definition of a product quality profile that represents in-vitro how the product will perform in-vivo. This profile is a quantitative representation of the clinical safety and efficacy targets for product developers.
• Summarizing and centralizing what is known about the Active Pharmaceutical Ingredient (API), excipients and process, and identifying knowledge gaps in order to prioritize study based on risk evaluation
• Design of the composition of the product and careful definition of the quality characteristics that need to be controlled in order to achieve the product quality profile
• Development of a flexible process for the product that has the defined quality characteristics
• Determination of key process specifications and starting material characteristics to be managed in order to achieve the desired end product characteristics. To have a flexible process requires that the acceptable performance envelope of the process is well defined and the boundaries of acceptability are well understood. A methodology, such as Design of Experiments (DoE), may be used to aid in process performance.
• Establishment of an overall control strategy for all aspects of the manufacturing process. Risk assessment for the various steps should be considered in the control process. PAT methodology may be employed as part of the overall control strategy.
• Constant auditing of the manufacturing process and revision, as necessary, to maintain final product quality

Historically, Quality by Testing (QbT) was utilized to develop processes. However, this approach has the following challenges:

Product quality and performance supported by end product testing leads to:

• Limited understanding of the process or key process parameters based on risk assessment
• Limited science-based understanding of process, managing of key variables and their affect on end-product quality.
• Limited understanding of process design space and relationship to product quality

In general, there is less time devoted to understanding why processes fail and more emphasis on testing and reworking out-of-spec product as necessary. Quality assured by testing and inspection also results in:

• Data intensive submission, disjointed information without consideration of the entire process
• Specifications based on batch history
• A validated and approved process discourages changes
• Focus on reproducibility, often avoiding or ignoring variations 

Quality by Design encompasses all major aspects of pharmaceutical production. In drug development, a methodical, multivariate approach is used to construct an effective process design based on assessing the risk associated with various steps. In the manufacturing process, a QbD strategy permits process flexibility within the defined design space. Process control effectively uses process analytical technology to track process trends and quality assurance is a necessity of risk-based control processes, which ensures less likelihood of batch failure.

• Quality built into product and process by design, based on scientific understanding
• Knowledge-rich submission – showing product knowledge and process understanding
• Specifications based on product performance requirements
• Flexible process within design space – allowing continuous improvement
• Focus on robustness – understanding and controlling variations

• “Right first time” – reduced costs and less process downtime
• Science-based understanding of the process results in minimized batch failure or rework
• Better consistency in drug quality and efficacy
• Ensures therapeutic efficacy of generics
• Reduced time to market for new drugs
• Less intensive regulatory oversight
• Process changes within approved design space is permitted without regulatory resubmission
• In-depth process understanding results in process improvement over time – improved yields, lower costs
• Real-time process monitoring and trending (with Process Analytical Technology (PAT)) reduces the analysis burden and improves the product quality 

• All stakeholders must be in agreement
• Corporate inertia
• Initial cost of new equipment and training
• Developing a Quality by Design-oriented information system to capture necessary documentation
• Supply chain issues
• Regulatory standards by which to judge Critical Quality Attributes (CQAs), analytical methods, etc.
• Global acceptance (or lack of)
• Lack of understanding of actual cost savings and positive business model 

Design, analyze and control manufacturing through timely measurements of critical quality and performance attributes of raw and in-process materials.

• Define design space via multivariate data analysis encompassing Critical Process Parameters (CPPs)
• Use inline or online Process Analytical Technology (PAT) to track CPPs (e.g. ReactIR, ReactRaman, EasySampler, ParticleTrack, EasyViewer, pH, calorimetry)
• Confirm that the process is operating within design space via process monitoring and trending
• Use knowledge derived from process performance monitoring to continually improve process within design space
• Enable real-time release of intermediates or an end product

Real-time analysis by FTIR or Raman spectroscopy provides critical reaction kinetic and trending information:

• Use in conjunction with Design of Experiments (DoE) for defining critical process parameters (CPPs) and design space
• Develop a control strategy for process and incorporate into production
• Ensure that the process is performing within operating space
• Support process validation efforts in process design, qualification and verification stages

Automated chemical reactors and automated sampling offer precise control and monitoring of reaction variables:

• Explore multiple process parameters simultaneously for fast, effective Design of Experiment (DoE) studies
• Incorporate inline automated sampling for accurate and representative, high-quality samples
• Investigate critical thermal parameters for safe and efficient process scale-up
• Seamless integration with in-situ spectroscopy and particle analyzers for thorough understanding of Critical Quality Attributes (CQAs) and defining Critical Process Parameters (CPPs)

Real-time, in-situ particle size analyzers provide important insight into the following crystallization processes:

• Define Critical Quality Attributes (CQAs) and Critical Process Parameters (CPPs) for all steps in the crystallization process
• Develop design space and process response to understand failure probabilities
• Develop control strategies for crystallization

The premise of Quality by Design (QbD) is that quality must be integrated into all aspects of a process and it cannot be achieved by simple analysis at certain points. To successfully achieve QbD means that extensive process knowledge must be developed, and PAT has a central role by providing the required real-time data. It is the synergy of PAT data and modeling capability of Dynochem and Reaction Lab that provide in-depth process knowledge.

Using data generated by PAT, Reaction Lab simultaneously models the effect of reaction variables to provide in-depth understanding on the best set of conditions to achieve the desired quality objectives. The response of the reaction or crystallization to varying specific parameters and conditions can be observed, and response surfaces generated that give insight into yield/impurity tradeoffs as well as overall robustness of the system. Dynochem modeling enables scale-up of reactions and crystallizations and identifies Critical Process Parameters (CPP) that achieve required Critical Quality Attributes (CQA). Dynochem reveals appropriate process conditions that ensure safe, sustainable operation and meet yield/impurity targets. Particularly important in-scale up and production, Dynochem aids in managing risk through the development of design space models for optimized operating conditions to achieve QbD objectives. 

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